Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics

Background: The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called ’spatial transcriptomics’. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce. Results: Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies. Conclusions: Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.


Supplementary
. The human cerebellus dataset was downloaded from 10x Genomics website. The tissue was stained with hematoxylin and aqueous eosin (HE) (A). Data were normalized using Seurat 3 , and pathways from MSIgDB 2 were scored using Single-cell Explorer Scorer 1 . Using Single-Cell Spatial Explorer, signatures of cell types (Viridis gradient heatmap) and tissue biology (Inferno gradient heatmap) are visualized through a min-max scale and opacity threshold (B).

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Supplementary Figure 4. The human spinal cord dataset was downloaded from 10x Genomics website. The tissue was stained by immunofluorescence using antibodies Anti-SNAP25 (green), Anti-GFAP (pink), Anti-Myelin CNPase (red), DAPI (blue) (A). Data were normalized using Seurat 3 , and pathways from MSIgDB 2 were scored using Single-cell Explorer Scorer 1 . Using Single-Cell Spatial Explorer, signatures of cell types (Viridis gradient heatmap) and tissue biology (Inferno gradient heatmap) are visualized through a min-max scale and opacity threshold (B). Figure 5. The mouse kidney dataset was downloaded from 10x Genomics website. The tissue was stained with hematoxylin and aqueous eosin (HE) (A). Data were normalized using Seurat 3 , and pathways from MSIgDB 2 were scored using Single-cell Explorer Scorer 1 . Using Single-Cell Spatial Explorer, signatures of cell types, tissue structure (Viridis gradient heatmap) and tissue biology (Inferno gradient heatmap) are visualized through a min-max scale and opacity threshold (B). Figure 6. The mouse brain dataset was downloaded from 10x Genomics website. Data were normalized using Seurat 3 , and signatures from MSIgDB 2 were scored using Single-cell Explorer Scorer 1 . Using Single-Cell Spatial Explorer, the specified behavioral signatures from GO-BP are visualized (Inferno gradient heatmap) through a min-max scale without opacity threshold .

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Supplementary Figure 7. Analysis of spatial ATAC-seq experiment. A: A mice sample named "ME11 H3K27me3 50um" (GSM5028434) was downloaded and data were processed as described in 4 to obtain a Seurat object. Then, genes scores were exported as a matrix, merged with spatial coordinate using the scExplorer Merger (supplementary Fig. 2) and visualized in scSpatial Explorer. B: Human tonsil sample (GSM528388) was downloaded and data were processed as described in 5 , to obtain a Seurat object. Then, genes scores were exported as a matrix, merged with spatial coordinate using the scExplorer Merger (supplementary Fig. 2) and analysed in Single-Cell Spatial Explorer. Gates drawn in 5 can be easily reproduced (up right) with Single-Cell Spatial Explorer lasso and polygon drawing tools, and differential analysis of gates versus other areas was performed (center). The spatial visualisation of 3 up-regulated genes and 3 down-regulated genes are shown.

Memory footprint
Memory usage was a priority in the development of Single-Cell Spatial Explorer. Indeed, spatial transcriptomics is constantly evolving and the resolution and the number of cells increases rapidly. For most functions, Single-Cell Spatial Explorer does not load the whole dataset in RAM but loads only a needed subset. A large data set will increase hard disk usage and slow down the software; but it will still be possible to display it with an adequate amount of RAM. Single-Cell Spatial Explorer RAM footprint analysis with memusage (see supplementary fig. 1) for 2798 × 202 dataframe and 2798 × 9885 dataframe, shows a memory usage < 400MB. This memory footprint is very low, so Single-Cell Spatial Explorer does not require a powerful computer at least for the current resolution of spatial transcriptomics.

Comparison with existing softwares
Seurat 3 is a R package designed for single-cell RNA-seq data exploration. It is a R package able to do pre-processing tasks (such as quality controls, data normalisation, samples aggregation and batch effect correction), data analysis (reduce the dimensionality with PCA/t-SNE/UMAP, cluster analysis, differential gene expression, ...) and data visualisation. Seurat is a very powerful tool targeting users with programming knowledge such as bio-informaticians with a solid R expertise.
The Giotto 6 package consists of two modules, Giotto Analyzer and Viewer, which provide tools to process, analyze and visualize single-cell spatial expression data. It is compatible with 9 different state-of-the-art spatial technologies, including in situ hybridization (seqFISH+, merFISH, osmFISH), sequencing (Slide-seq, Visium, STARmap) and imaging-based multiplexing/proteomics (CyCIF, MIBI, CODEX). Data analysis is performed first in Giotto Analyzer using command lines and exported then to Giotto viewer. A solid experience in R is needed to install Giotto since many R and Python packages are needed and some dependencies are not listed in the installation note 1 . In the Giotto Analyzer, the image alignment should be done by trial and error: the place and the zoom of the image are controlled by numerical parameters. Giotto Analyzer can filtrate genes ans cells for quality controls. At the time of the manuscript, the link to the Giotto viewer was broken and the documentation "How to switch between Giotto Analyzer and Viewer?" was not available with the status "work in progress". ST viewer 7 is a compiled software to perform analysis and visualization of spatial transcriptomic datasets. The ST viewer enables users to visualize the location of one or multiple genes in real time in a stand-alone desktop application. ST viewer can filter and normalize data with DESeq2, reduce the dimensionality with t-SNE or PCA and compute clusters. A binary for linux is not available. Unfortunately we were not able to test ST viewer on Windows since it does not start. This issue might be related to the R version though compatible versions are not documented and remain unanswered in the dedicated forum. We opened an issue during manuscript preparation. The Mac version need QT recompilation which is not accessible to the average user.
Vitessce 8 (Visual integration tool for exploration of spatial single cell experiments) is an open-source interactive visualization framework for exploration of multimodal and spatially resolved single-cell data; It presents a modular architecture compatible with transcriptomic, proteomic, genome-mapped, and imaging data types. In contrast to scSpatial Explorer, Vitessce can be used as a standalone web application or in Python or R environments. This software has nice online demos with 8 data sets.
Loupe Cell browser is a dedicated visualization and analysis tool for scRNAseq developed for analysing scRNAseq datasets produced by 10xGenomic platforms. It allows importing datasets and visualizing custom projections of either gene expression or antibody-only datasets, across t-SNE or UMAP computed by the Cell Ranger pipeline. Loupe Cell browser also provides Moran's I for pattern analysis of single gene expression level views across the image. Despite its ease of use however, this tool does not provide signature visualizations, nor the corresponding image pattern analyses. SquidPy 9 is a Python library dedicated to the analysis and visualisation of single-cell spatial transcriptomics datasets. The installation as only been tested on Linux, and requires some basic Python knowledge. This library is used after Scanpy QC & normalisation of the spatial dataset. This powerful tool gives a lot of analytic possibilities, such as cluster annotation, cluster features computation, neighborhood enrichment or even ligand-receptor interaction analysis. This tool also offers the possibility to integrate other libraries to perform complementary analysis. SquidPy has an easy-to-use graphical interface, but its main analytic features are only available through command line, eventually preventing some users to explore their data to the fullest.
Commercial softwares, such as BBrowser or Partek Flow are powerful analysis and visualisation softwares for single cell transcriptome analysis and spatial transcriptomics. However, since they are proprietary, not open source, and their cost is not specified in their website, they could not be tested here.
So currently, quite a few existing open source tools allow to visualize single cell spatial transcriptomics data (see software comparison in supplementary Table 1). We found two softwares, Loupe and STviewer, which are free of charge 2 and provide a ready-to-use binary executable like Single-Cell Spatial Explorer. Unfortunately we had issues with the compiled binaries of STviewer and Loupe interoperability is very far from what is possible with Single-Cell Spatial Explorer. By being mainly dedicated to data visualization, Single-Cell Spatial Explorer is mostly recommended for biologists, pathologists and biomedical users that are not familiar with R or command line tools. In conclusion, Single-Cell Spatial Explorer is currently the best alternative for users who want a software which works out of the box, without tedious installation.